Combining color and spatial information for object recognition across illumination changes

Muselet, Damien; Macaire, Ludovic

doi:10.1016/j.patrec.2007.02.001

Cited by 17 publications

(9 citation statements)

References 19 publications

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“…Fast colour-texture segmentation is required for instance by on-board low-level image analysis algorithms running in real time, such as for mobile robots control [37] or road detection in vehicle environment [38]. But CCMs are known to be sensitive to illumination variations [39], and CCMbased texture classification only provides satisfying results when the illumination is constant. This is a key issue in the analysis of outdoor scene images.…”

Section: Resultsmentioning

confidence: 99%

Colour texture classification from colour filter array images using various colour spaces

Losson

Macaire

2012

IET Image Processing

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This paper focuses on the classification of colour textures acquired by single-sensor colour cameras. In such cameras, the Colour Filter Array (CFA) makes each photosensor sensitive to only one colour component, and CFA images must be demosaiced to estimate the final colour images. We show that demosaicing is detrimental to the textural information because it affects colour texture descriptors such as Chromatic Co-occurrence Matrices (CCMs). However, it remains desirable to take advantage of the chromatic information for colour texture classification. This information is incompletely defined in CFA images, in which each pixel is associated to one single colour component. It is hence a challenge to extract standard colour texture descriptors from CFA images without demosaicing.We propose to form a pair of quarter-size colour images directly from CFA images without any estimation, then to compute the CCMs of these quarter-size images. This allows us to compare textures by means of their CCM-based similarity in texture classification or retrieval schemes, with still the ability to use different colour spaces. Experimental results achieved on benchmark colour texture databases show the effectiveness of the proposed approach for texture classification, and a complexity study highlights its computational efficiency.

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Section: Resultsmentioning

confidence: 99%

Colour texture classification from colour filter array images using various colour spaces

Losson

Macaire

2012

IET Image Processing

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“…Similar questions have been raised and answered for skin pixel segmentation [9][10][11], shadow and traffic object detection [12][13][14] and image segmentation by graph cut in [15,16]. Choice of color spaces influences object recognition see [17] for more details.…”

Section: Introductionmentioning

confidence: 86%

Analytical Study of Colour Spaces for Plant Pixel Detection

Kumar¹,

Miklavcic²

2017

Preprint

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Segmentation of a region of interest is an important pre-processing step for many colour image analysis techniques. Similarly segmentation of plant in digital images is an important preprocessing step in phenotying plants by image analysis. In this paper we present an analytical study to statistically determine the suitability of colour space representation of an image to best detect plant pixels and separate them from background pixels. Our hypothesis is that the colour space representation in which the separation of the distributions representing plant pixels and background pixels is maximized would be the best for detection of plant pixels. The two classes of pixels are modelled as a Gaussian mixture model (GMM). In our GM modelling we don't make any prior assumption about the number of Gaussians in the model. Rather a constant bandwidth mean-shift filter is used to cluster the data and the number of clusters and hence the number of Gaussians is automatically determined. Here we have analysed following representative colour spaces like RGB, rgb, HSV, Ycbcr and CIE − Lab. This is because these colour spaces represent several other similar colour spaces and also an exhaustive study of all the colour space will be too voluminous. We also analyse the colour space feature from the two-class variance ratio perspective and compare the results of our hypothesis with this metric. The dataset for this empirical study consist of 378 digital images of plants and their manual segmentation. Dataset consist of various species of plants (arabidopsi, tobacco, wheat, rye grass etc.) imaged under different lighting conditions, indoor and outdoor, controlled and uncontrolled background. In results we obtain better segmentation of the plants in HSV colour space, which is supported by its Earth mover distance (EMD) on the GMM distribution of plant and background pixels.

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“…Similar questions have been raised and answered for skin pixel segmentation [9][10][11], shadow and traffic object detection [12][13][14] and image segmentation by graph cut [15,16]. It has been shown in [17] that the choice of colour space does influence object recognition.…”

Section: Introductionmentioning

confidence: 87%

Analytical Study of Colour Spaces for Plant Pixel Detection

Kumar

Miklavcic

2018

J. Imaging

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Segmentation of regions of interest is an important pre-processing step in many colour image analysis procedures. Similarly, segmentation of plant objects in digital images is an important preprocessing step for effective phenotyping by image analysis. In this paper, we present results of a statistical analysis to establish the respective abilities of different colour space representations to detect plant pixels and separate them from background pixels. Our hypothesis is that the colour space representation for which the separation of the distributions representing object and background pixels is maximized is the best for the detection of plant pixels. The two pixel classes are modelled by Gaussian Mixture Models (GMMs). In our statistical modelling we make no prior assumptions on the number of Gaussians employed. Instead, a constant bandwidth mean-shift filter is used to cluster the data with the number of clusters, and hence the number of Gaussians, being automatically determined. We have analysed the following representative colour spaces: RGB, rgb, HSV, Ycbcr and CIE-Lab. We have analysed the colour space features from a two-class variance ratio perspective and compared the results of our model with this metric. The dataset for our empirical study consisted of 378 digital images (and their manual segmentations) of a variety of plant species: Arabidopsis, tobacco, wheat, and rye grass, imaged under different lighting conditions, in either indoor or outdoor environments, and with either controlled or uncontrolled backgrounds. We have found that the best segmentation of plants is found using HSV colour space. This is supported by measures of Earth Mover Distance (EMD) of the GMM distributions of plant and background pixels.

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Combining color and spatial information for object recognition across illumination changes

Cited by 17 publications

References 19 publications

Colour texture classification from colour filter array images using various colour spaces

Colour texture classification from colour filter array images using various colour spaces

Analytical Study of Colour Spaces for Plant Pixel Detection

Analytical Study of Colour Spaces for Plant Pixel Detection

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